Datasets:
File size: 8,809 Bytes
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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import pandas as pd\n",
"import os, json\n",
"from transformers import pipeline\n",
"from tqdm import tqdm"
]
},
{
"cell_type": "markdown",
"source": [
"## Load the dataset"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "code",
"execution_count": null,
"outputs": [],
"source": [
"from datasets import load_dataset\n",
"\n",
"dataset = load_dataset(\"generative-newsai/news-unmasked\")\n",
"\n",
"# Train and test split\n",
"test_dataset = dataset[\"test\"]\n"
],
"metadata": {
"collapsed": false,
"pycharm": {
"is_executing": true
}
}
},
{
"cell_type": "markdown",
"source": [
"## Check first 5 rows of the Test dataset"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "code",
"execution_count": 3,
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'image': [<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=1050x550 at 0x13796D490>, <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=1050x550 at 0x137977700>, <PIL.PngImagePlugin.PngImageFile image mode=RGB size=789x412 at 0x137977910>, <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=1050x549 at 0x1379771C0>, <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=1050x550 at 0x137977640>], 'section': ['Television', 'Sports', 'Television', 'Technology', 'Fashion & Style'], 'headline': [\"What 's on TV Monday : ' The Voice ' and ' Gentefied '\", 'Phillies - Blue Jays Games Postponed After 2 Staff Members Test [MASK]', \"Joe Biden 's Run Has [MASK] Night Looking for a Fight\", 'Pinterest Posts [MASK] Loss , but Falls Short of Wall St. Estimates', 'Yolanda Foster : Watching Her Daughter Gigi Hadid From the Front Row'], 'image_id': ['00008455-a932-5f2c-b5ce-86584d8345b0', '00040f12-c19e-54db-9513-d4a3d9ce30f1', '0006d6e6-a16f-5d69-a307-0e7e1b659075', '000755c6-df96-502a-a3e4-8f78f0919a8c', '001b8cc1-c623-571c-9e2d-86e1f3f7c20c']}\n"
]
}
],
"source": [
"# Check first 5 rows of the test dataset\n",
"sample_data = test_dataset[:5]\n",
"print(sample_data)"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "markdown",
"source": [
"## Load the model"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "code",
"execution_count": 5,
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Some weights of the model checkpoint at mlcorelib/deberta-base-uncased were not used when initializing BertForMaskedLM: ['cls.seq_relationship.bias', 'cls.seq_relationship.weight']\n",
"- This IS expected if you are initializing BertForMaskedLM from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
"- This IS NOT expected if you are initializing BertForMaskedLM from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n"
]
}
],
"source": [
"model_name = \"mlcorelib/deberta-base-uncased\"\n",
"unmasker = pipeline('fill-mask', model=model_name)"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "markdown",
"source": [
"## Unmask the sentences"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "code",
"execution_count": 6,
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"100%|██████████| 12247/12247 [05:47<00:00, 35.20it/s]\n"
]
}
],
"source": [
"all_masked_words = []\n",
"for each_dict in tqdm(test_dataset):\n",
" sentence = each_dict['headline'] # Get the sentence from the dictionary\n",
" image_id = each_dict['image_id'] # Get the image_id from the dictionary\n",
" if \"[MASK]\" in sentence: # See if it has a [MASK] in headline\n",
" result = unmasker(sentence) # Unmask the sentence\n",
"\n",
" # Make a list of indices where [MASK] is present in the sentence\n",
" # If there are more than one [MASK] in the sentence, then add them as separate entries in the result list\n",
" indices = [i for i, x in enumerate(sentence.split()) if x == \"[MASK]\"]\n",
" if len(indices) > 1:\n",
" masked_word_idx_list = []\n",
" for i, each_result in enumerate(result):\n",
" # Get the top scoring word\n",
" top_word = each_result[0]['token_str']\n",
" all_masked_words.append([image_id, indices[i], top_word])\n",
" else:\n",
" all_masked_words.append([image_id, indices[0], result[0]['token_str']])\n",
"\n",
"final_masked_words = [l[0] for l in all_masked_words]"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "markdown",
"source": [
"## Print first 5 rows of the masked words list"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "code",
"execution_count": 13,
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"['00040f12-c19e-54db-9513-d4a3d9ce30f1', '0006d6e6-a16f-5d69-a307-0e7e1b659075', '000755c6-df96-502a-a3e4-8f78f0919a8c', '0038ee8b-3f57-5838-a201-509d4bcd1c06', '0048f974-e081-54ee-b0c4-e30b5f66c763']\n"
]
}
],
"source": [
"print(final_masked_words[:5])"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "markdown",
"source": [
"## Save the results as a dataframe and print first 5 rows of the dataframe"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "code",
"execution_count": 15,
"outputs": [
{
"data": {
"text/plain": " id token_index token\n0 00040f12-c19e-54db-9513-d4a3d9ce30f1 11 .\n1 0006d6e6-a16f-5d69-a307-0e7e1b659075 5 the\n2 000755c6-df96-502a-a3e4-8f78f0919a8c 2 a\n3 0038ee8b-3f57-5838-a201-509d4bcd1c06 0 regular\n4 0048f974-e081-54ee-b0c4-e30b5f66c763 6 a",
"text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>id</th>\n <th>token_index</th>\n <th>token</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>00040f12-c19e-54db-9513-d4a3d9ce30f1</td>\n <td>11</td>\n <td>.</td>\n </tr>\n <tr>\n <th>1</th>\n <td>0006d6e6-a16f-5d69-a307-0e7e1b659075</td>\n <td>5</td>\n <td>the</td>\n </tr>\n <tr>\n <th>2</th>\n <td>000755c6-df96-502a-a3e4-8f78f0919a8c</td>\n <td>2</td>\n <td>a</td>\n </tr>\n <tr>\n <th>3</th>\n <td>0038ee8b-3f57-5838-a201-509d4bcd1c06</td>\n <td>0</td>\n <td>regular</td>\n </tr>\n <tr>\n <th>4</th>\n <td>0048f974-e081-54ee-b0c4-e30b5f66c763</td>\n <td>6</td>\n <td>a</td>\n </tr>\n </tbody>\n</table>\n</div>"
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Save the results in a dataframe with column name id,token_index,token\n",
"df = pd.DataFrame(all_masked_words, columns=['id', 'token_index', 'token'])\n",
"df.head()"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "markdown",
"source": [
"## Save the dataframe as a csv file"
],
"metadata": {
"collapsed": false
}
},
{
"cell_type": "code",
"execution_count": 16,
"outputs": [],
"source": [
"df.to_csv('sample_result.csv', index=False)"
],
"metadata": {
"collapsed": false
}
}
],
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"display_name": "Python 3",
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